High-Throughput Optical Sensing Immunoassays on Smartphone

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High-Throughput Optical Sensing Immunoassays on Smartphone Li-Ju Wang, Rongrong Sun, Tina Vasile, Yu-Chung Chang, and Lei Li Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.6b02211 • Publication Date (Web): 19 Jul 2016 Downloaded from http://pubs.acs.org on July 24, 2016

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Analytical Chemistry

High-Throughput Optical Sensing Immunoassays on Smartphone Li-Ju Wang†, Rongrong Sun†, Tina Vasile‡, Yu-Chung Chang†, and Lei Li*† † ‡

School of Mechanical and Materials Engineering, Washington State University, Pullman, WA 99164, USA Irrigated Agriculture Research and Extension Center, Washington State University, Prosser, WA 99350, USA

We present an optical sensing platform on smartphone for high-throughput screening immunoassays. For the first time, a designed microprism array is utilized to achieve a one-time screening of 64 samples. To demonstrate the capability and the reliability of this optical sensing platform on smartphone, human interleukin 6 (IL-6) protein and six types of plant viruses are immunoassaying. The ability of quantification is shown by a sigmoidal dose-response curve fitting to analyze IL-6 protein. The accuracy in measuring the concentrations of IL-6 protein achieves 99.1%. On the other hand, to validate on-field immunoassays by our device, total 1,030 samples are assayed using three immunoassay methods to detect six types of plant viruses. The accuracy is up to 96.2% ~ 99.9% as well as a high degree of agreement with lab instruments. The total cost for this high-throughput optical screening platform is ~ $50 USD. The reading time is only 2 seconds for 64 samples. The size is just as big as a portable hard drive. Our optical sensing platform on smartphone offers a route toward in-situ high-throughput screening immunoassays for viruses, pathogens, biomarkers, and toxins by decentralizing laboratory tests. With this mobile point-of-care optical platform, the spread of disease can be timely stopped within a very short turnaround time. In remote areas (outside laboratories, under-developed, or low-resource areas), owing to lack of reading equipment, turnaround time, including back-and-forth, waiting, ready queue, and testing time, can take several working days for immunoassays to diagnosis diseases. In these areas, samples must be collected and sent back to central laboratories or taken to a well-equipped hospital. These inefficient and inconvenient routine procedures are laborious, time-consuming and high cost. Most importantly, these delays can cause a failure to make prompt decisions and treatments in infectious diseases. To meet the needs of decentralized laboratory testing, a combination of mobile health (mHealth) and a point-of-care technology (POCT), called mobile point of care technology (MPOCT), is booming. MPOCT provides patients accurate diagnosis/testing immediately on-site, and provides clinicians with rapid testing and transmittance of results to enhance clinical decisions in situ. 1 ,2 M-health has been defined by the Global Observatory for e-health of the World Health Organization (WHO) as "medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants and other wireless devices".3 Breaking the limitations of locations, resources, and conventional testing turnaround times, MPOCT is changing the current treatment paradigm and the diagnostic procedures by immunoassaying virus, pathogen, biomarkers, toxicity/pollution, etc. There are several types of MPOC devices in recent years, such as smartphone paper test stripe readers, 4 smartphone colorimetric readers, 5 smartphone optical spectrum readers,6,7 smartphone surface plasmon resonance detector, 8 smartphone ultrasound devices, 9 smartphone microscopes, 10 etc. However, most MPOC devices could only measure one sample each time. For high-throughput assays, microplate-based methods are the most common used. Traditional bench-top microplate readers scan and illuminate wells one-by-one and take optical

measurement in each well via a sophisticated optical design. According to this optical sensing mechanism, Ozcan's group developed a cellphone-based microplate colorimetric reader for enzyme-linked immunosorbent assays (ELISA). 11 A blue light-emitting-diode (LED) array was used as the illumination source with 96 optical fibers to collect signal from each well of a microplate. In order to function correctly, the 96 optical fibers needed to be precisely aligned with each well. Some other researchers have utilized imaging-based analysis instead of the traditional optical mode.12,13 For MPOCT, integrated with the smartphone technology, Vashist's group demonstrated a smartphone-based colorimetric reader using the screen-based bottom illumination to measure multiple samples at the same time. 14 They used an iPAD mini, iPAD4 and iPhone 5s, respectively, as the illumination source and a smartphone (Samsung Galaxy SIII mini) to take a picture of the whole microplate or several strips for colorimetric analysis. In this paper, we present a low-cost, compact, and highthroughput optical sensing platform on smartphone for biomedical, plant, animal and environmental diagnosis in situ by immunoassays. The one-time screening of 64 samples is detected using our designed high-throughput smartphone optical platform (HiSOP) integrated with a microprism- array to meet the need of high-throughput screening. To precisely detect 64 samples one time using the smartphone camera sensor, the crucial challenge is the field-of-view (FOV) un-matching between the smartphone camera and the samples. When attempting to achieve a wide FOV, the distance between the smartphone camera and the microplate is large, and it causes the whole setup to be bulky and the resolution decreased. Miniaturizing the reading distance causes the FOV to not be wide enough, leading to the wells around the corners and edges of the plate being unsuitable for assaying. Illustrated in Figure 1(a), iPhone 5 is using a 4.1 mm lens to give a 33 mm equivalent FOV. However, a 96-well microplate is 85.5 mm in width

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and 127.8 mm in length. Each time, the camera sensor could only precisely detect 4 samples on the 96-well plate. In Figure 1(b), captured by using a smartphone back camera, only 4 wells in the center can be fully seen, and the wells on the outside are partially or even fully blocked. One-time screening of 64 wells requires a wider FOV. To solve this FOV unmatching problem, there are several possible methods to redirect the light beams: fiber optics arrays,5 decentered lens arrays,15 prism arrays,16,17 or beam steering mirrors. Our innovation uses a microprism array to tilt the light from each well of a microplate into a smartphone camera in Figure 1(c). Integrated with this microprism array, optical signals from all the wells can be clearly and fully captured, as can be seen from the picture in Figure 1(d).

Figure 1. (a) Illustration of a narrow FOV of smartphone camera sensor, which could only detect 4 samples each time. (b) The image of a microplate only captured by the camera. (c) Illustration of a wider FOV using a microprism array, which could detect 64 samples one time. (d) The captured image of a microplate with a microprism array.

A designed microprism array plays a crucial role to direct light from each well in a microplate. Table S1 (Supporting Information) shows the titling angles and light deviation angles (in the brackets) designed for an 8 x 8 well array. Since the microprism array is symmetric about the center in both vertical and horizontal directions, each microprism is tilted toward the center of the array. Polydimethylsiloxane (PDMS) is used to fabricate the microprism array. The tilting angles were calculated by using 1.39 as material refractive index and by setting the working distance as 100 mm between the camera and the microprism array. The four wells in the center do not need light deviation, and as such the tilting angles are zero. The principle of a microprism is schematically shown in Figure 2(a). The incident light beam will be deviated to an angle σ, calculated by Eqn. (1):

tan

(1)

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where δ is the angle of refraction (prism apex angle), nprism is the refractive index of the prism material, and n0 is the refractive index of air. To enlarge the FOV of a microplate so that every well can be clearly seen by the camera sensor, a prism array is designed as shown in Figure 2(b). Each microprism in this array is designed to tilt the light of one well to match the FOV of the imaging camera. The titling angles and directions are different for each microprism depending on the distance and location of the well to the center of the camera. The designed microprism array can solve the FOV un-matching problem and achieve a miniaturized high-throughput screening MPOC device.

To validate our HiSOP, we assayed an important cancer biomarker, human interleukin 6 (IL-6), by enzyme-linked immunosorbent assays (ELISA). IL-6 has been extensively shown as a relevant cancer biomarker for breast, 18 , 19 prostate, 20 , 21 liver, 22 , 23 epithelial, 24 and lung cancers. 25 , 26 All readouts from the HiSOP were compared with readouts by a laboratorial microplate reader. These tests showed the utility of the HiSOP for performing broad types of quantitative, homogeneous, colorimetric analysis. Plant viruses cause infectious diseases that lead to large economic losses. Therefore, early and rapid detection of viruses are of great importance. 27 In reality, plant viruses vary among transformed plants and viral titers change seasonally.28, 29 In field-testing of plant viruses, to make a rapid diagnostic decision, ELISA assays read from the microplate reader is indispensible. To assess the feasibility of the HiSOP in field immunoassays, we tested six types of plant viruses using a total of 1,030 plant samples from seeds, bud woods, pollens, and leaves by three ELISA methods.30, 31 As the alternative to traditional laboratorial readers, the reliability and validity of the HiSOP were examined in this work.

EXPERIMENTAL SECTION Fabrication of the microprism array. To fabricate the designed microprism array, a hybrid manufacturing process was illustrated in Scheme 1. In this process, a negative mold of the microprism array was first printed by using a fused deposition 3D printer (uPrint SE Plus, Stratasys, Eden Prairie, MN, USA). Printing material was acrylonitrile-butadiene-styrene (ABSplus-P430, Stratasys, Eden Prairie, MN, USA) and the mold was printed as a solid part. By using the layer of thermoplastic sheet, the surface quality of the microprism array mold can be improved. Vacuum forming was then used to generate a smooth uniform layer of thermoplastic sheet (Polyethylene terephthalate, PET sheet, thickness 1/32 inch, widgetworks, NY, USA) on the surface of the printed mold. A compact vacuum forming machine (JT-8 Vacuum Forming Machine, China) was used. Each individual prism mold was designed protruding from the bottom substrate. That way, vacuum forming can form the complete inclined prism surface. Due to the size


2

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limitation of this vacuum forming machine, only an 8 x 8 prism array was fabricated. In the future, the size of microprism array could be extended to 96 wells (8 x 12 arrays) using the larger vacuum forming machine. After vacuum molding, PDMS (SYLGARD®184 silicone elastomer kit, base and curing agent mixed at a ratio 10:1, Dow Corning) was poured into this mold and fully curing at 60˚C for 2 hours, a transparent prism array was fabricated after peeling off. Scheme 1. The hybrid manufacturing process for the microprism array 3D Printer

PETcovered mold

Heater PET Sheet

PDMS Casting

demonstrated in Figure 4 (b). In this App, four alignment circles are used to align with the wells on the four corners. With this alignment function, the captured images will show the same well positions. For data processing, our App can be connected to a cloud-based server. At this initial development stage, we used a MATLAB (Mathworks, MA, USA) Graphical User Interface (GUI) program instead of the server. Each time, 5 pictures are taken continuously by the App. In digital color images, red (R), green (G), and blue (B) are three main channel types (color models). In this work, we assess G and B channels independently by two different color dyes. In each well, the light intensities of a circular area located in the center of each well (a radius of 25 pixels), are read by the self-built MATLAB program. The average light intensity is analyzed and converted to absorbance by calculating the 5 pictures of each well and normalizing to the light intensity of deionized (DI) water.

Air pumped out

(a)

Setup of high-throughput smartphone optical platform. The assembled setup of HiSOP is shown in Figure 3 and constructed with the total size of 142 mm x 160 mm x 41 mm. A low-cost LED backlight panel (112 mm x 88 mm x 3 mm, 24V DC) is used as the illumination light source which greatly reduces both the cost and size of the HiSOP. The main HiSOP body is a holder for a 96-well microplate and other components including a prism array, two aperture arrays, and one backlight panel. Inside the holder, two aperture arrays are prealigned with the microplate position and fixed by gluing. One aperture array was placed beneath the microplate with the other aperture array placed on top of the microplate. Each aperture on the array was aligned with each well of the microplate, and the LED backlight panel was fixed beneath the bottom aperture array. The prism array was well aligned above the top aperture array and was pressed tightly by a top cover lid. Both the main holder and the battery holder were 3D printed by using opaque ABS. Two black-painted aperture arrays were made by laser cutting two pieces of acrylic sheet, 1/32 inch thick, with an aperture diameter of 6 mm. One top and one bottom cover lids were laser cut by using a 1/16 inch thick acrylic sheet.

Figure 3. The assembled setup of HiSOP.

Image acquisition and data processing. An iPhone mobile application (App) was developed as a companion for the HiSOP. Because the smartphone camera can automatically adjust camera parameters which lead to unwanted and inconsistent data errors, our self-built App can manually control camera parameters, such as focus, ISO value, exposure time, and white balance in Figure 4(a). The other function of this App is to align the smartphone camera with the samples,

(b)

Figure 4. (a) The self-built App controlling image capturing parameters. (b) The alignment function in our self-built App.

Preparation of diluted serial color dye solutions. Rhodamine B (RhB, ≥95%), purchased from Sigma-Aldrich (St. Louis, MO, USA), was serially diluted in DI water from 11 to 1 ng/µL with a total of 55 samples and DI water used as the reference. Curcumin (≥98%), purchased from Acros Organics (Geel, Belgium), was serially diluted in pure ethanol (200 proof) from 0.4 to 10.0 ng/µL with a total of 87 samples and ethanol was used as the reference. Human IL-6 immunoassays. The commercial human IL-6 in human serum ELISA kit was purchased from Invitrogen (Carlsbad,CA, USA). We followed standard procedures to measure human IL-6 level in human serum with 14 samples.32 The concentration of serial dilute IL-6 standards in human serum ranges from 250 to 6 pg/mL. All samples were measured by our HiSOP and the laboratory microplate reader (Tecan Safire2, Männedorf, Switzerland) for two replicates. The presented absorbance was the average values. Plant virus field-immunoassays. Six types of plant viruses were assayed by three ELISA methods as follows: (1) Grapevine leaf roll-associated virus 3 (GLRaV3), a virus that leads to the most important grapevine viral diseases in the genus Closterovirus.33,34 (2) Cherry leaf roll virus (CLRV), a pollentransmitted virus located within pollen grains.35 CLRV often infects a variety of deciduous trees and shrubs in forested areas. 36 , 37 After grinding infected/healthy leaf tissue or bud woods, GLRaV3 and CLRV were assayed by direct antibody sandwich ELISA respectively. 38,39 (3) Bean common mosaic


3


(B)

HiSOP (NRhB=55) y = 0.229x + 0.135 R² = 0.982

AB

AG

2

%&'(& %)*+, (%&'(& -%)*+, )⁄

(2)

Where /012310 and /4567 are absorbance measured by the lab reader and the HiSOP, respectively. The mean and the standard deviation of these differences are presented by δ8 and s, respectively. For a large sample size greater than 60, the 95% limit of agreement is calculated by δ8 ± 1.96=. For sample size less than 60, the formula is δ8 ± 2=.54 B&A recommended that 95% of the difference data points should lie within the limits

2

HiSOP (Ncurcumin=87) y = 0.147x + 0.096 R² = 0.992

1.5 1

1 0.5 0

0 0

2

4 6 8 10 RhB Conc. (ng/µL)

12

2 4 6 8 Curcumin Conc. (ng/µL)

10

Lab Reader (Ncurcumin=87)

1.4

y = 0.129x + 0.069 R² = 0.987

1.2

y = 0.211x + 0.154 R² = 0.986

2

1

A405

1.5

A553

0

Lab Reader (NRhB=55)

2.5

1

0.8 0.6 0.4

0.5

0.2

0

0 0

2

4 6 8 10 RhB Conc. (ng/µL)

12

0

Linear Correlation (RhB)

2.5

2 4 6 8 Curcumin Conc. (ng/µL)

10

Linear Correlation (Curcumin) 1.4

2

1.2 A405 of lab reader

y = 0.950x R² = 0.981

1.5

Assessment of HiSOP by color dye solutions. Two sets of color dye solutions were tested to assess G and B channels in color imaging processing on our HiSOP. Figure 5 presents the measurements of two color dyes, RhB and curcumin, by HiSOP and lab reader. Columns (A) and (B) in Figure 4 show the linear regression analysis of a total sample number, N, 55 RhB solutions and 87 curcumin solutions. The absorbance peak of the RhB solution is at 553 nm wavelength (A553) whose light channel is green (AG). In column (A), we validated HiSOP performance in G channel. In column (B), the absorbance peak of curcumin solution is at 405 nm wavelength (A405) whose light channel is blue (AB). The linear regression of the HiSOP in G and B channels both achieved relatively high accuracy levels, 98.2% and 99.2% respectively, and were comparable to the results from the lab reader (98.6% and 98.7%). The linear correlations (R2) between the HiSOP and lab readers are 0.981 in G channel and 0.991 in B channel, indicating very strong association between readers for these channels. The other common approach in the clinical laboratory to analyze the agreement between two instruments is to examine the differences in a Bland-Altman plot (B&A plot).52, 53 The difference percent (%) was computed by Eqn. (2): Difference percentage (%) 100 ×

(A) 3

1

0.5

y = 0.859x R² = 0.991

1 0.8 0.6 0.4 0.2 0

0 0

0.5

1

1.5

2

0

2.5

δ8 + 2= 26%

30 20 10

δ8 6%

0

98.2% Agree

-10 δ8 − 2= −14%

-20

1

1.2 1.4

B&A Plot (Curcumin)

B&A Plot (RhB)

40

0.2 0.4 0.6 0.8 AB of HiSOP

AG of HiSOP

-30

Difference percent (%)

RESULTS AND DISCUSSION

of agreement. The B&A plots of RhB and curcumin demonstrated that the differences data points are satisfied within the limits (98.2% and 97.7% respectively). By assessing G and B channels in the color imaging process, we successfully demonstrated the ability of quantification and the reliability by the HiSOP.

A553 of lab reader

virus – poty virus (BCMV-POTY), the most serious and widespread seed-transmitted type in beans.40 , 41 The tested beans were first soaked overnight and then ground, followed by indirect ELISA procedures using the poty virus antibodies. 42, 43 (4) Apple mosaic virus (ApMV), the most widespread apple virus, especially U.S. apple tree malus domestica. Since apple is the most widely grown fruit crops in Washington State, fast screening and elimination of apple mosaic disease is economically important to commercial apple cultivars.44, 45 A doubleantibody sandwich (DAS) ELISA was utilized for assaying the tested pollens.46 (5) Prune Dwarf Virus (PDV) and (6) Prunus necrotic ringspot virus (PNRSV), frequently encountered viruses infecting stone fruit trees, such as peach and cherry.47,48 Both viruses could be transmitted via budding, pollens, or grafting with infected wood. To detect PDV and PNRSV, respectively, DAS ELISA was performed.49, 50 All tested samples were field sampled from farmers in the state of Washington, and were measured by our HiSOP and the laboratory microplate reader (Multiskan Ascent, Thermo Fisher Scientific, Waltham, MA, USA) for two replicates. The presented absorbance was the average values. In field testing, there are no known standard concentration solutions to quantify virus infectious level. Therefore, the decision-making mainly relies on the results of reading instruments. The International Seeds Testing Association (ISTA) recommends using a negativepositive threshold of 2.5 times the background of healthy plant samples. 51 Following this universal rule, we interpreted a readout 2.5 times higher than that of the control/untreated group as positive.


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30 25

δ8 + 1.96= 24%

20 15 10

97.7% Agree

δ8 12%

5 0 -5

δ8 − 1.96= 0%

-10 0

1 2 Mean absorbance

3

0

1 Mean absorbance

2

Figure 5. Linear regression and B&A plots of (A) RhB solutions absorbed in green channel (AG) and (B) curcumin solutions absorbed in blue channel (AB) read by HiSOP and compared with the lab reader at wavelength 553 nm and 405 nm, respectively.

Validation of HiSOP by immunoassaying human IL-6 biomarker. For quantification of immunoassays, human IL-6 concentrations were analyzed by the HiSOP in B channel (AB) and by the lab reader at 450 nm wavelength shown in Figure 6. The readouts were fitted by a sigmoidal dose-response curve, also called a four-parameter logistic (4-PL) equation governed by Eqn. (3): ?(@) A +

CD E-(F GHI J)K

(3)

where ?(@) is the corresponding absorbance either by the HiSOP or the lab reader. x represents IL-6 concentration, with


4

blocks in the figures. The positive results were consistent by both instruments. All analytical results of the three types of ELISA methods show that the HiSOP and the lab reader are highly correlated with high degree of agreement.

y = 0.947x R² = 0.963

0.4 0.2 0.0 0.0

Lab Reader (N=14)

1.8

1.2

0.8

1 0.8

0.6

0.6

0.4

0.4

0.2

0.2 100


1.6

y = 1.027x R² = 0.993

1.2 0.8 0.4 0.0 0.0

0.4

0.8 1.2 1.6 AB of HiSOP

20

0.1

0.2 0.3 0.4 AB of HiSOP

2.0

30 20 10 0 -10 -20 -30 -40 -50

100% Agree

δ8 −7%

0.5

Linear Correlation for BCMVPOTY Virus (N =535)

(C)6 5

y = 1.045x R² = 0.987

4 3 2 1 0

δ8 − 2= −32% 0

1 2 Mean absorbance

0 3

Figure 6. Human IL-6 immuoassay calibration curve fitted by 4PL curve read by the HiSOP in blue channel (AB) and the lab reader at wavelength 450 nm (A450), respectively. The linear regression and B&A plot are shown the strong correlation and reliability.

(D)

1

5

0.6

y = 0.948x R² = 0.962

0.4 0.2

98.3% Agree

δ8 −16%

-40 -60

δ8 − 1.96= −57%

-80 0.0

0.2 0.4 Mean absorbance

0.6

B&A Plot (BCMV-POTY Virus) δ8 + 1.96= 17%

30 20 10 0 -10 -20 -30 -40 -50 -60 -70

98.5% Agree

δ8 −15% δ8 − 1.96= −47% 0

1

2

3 4 5 6 7 Mean absorbance

8

B&A Plot (ApMV)

60

δ8 + 1.96= 30%

40 20 0

100% Agree

δ8 −13%

-20 -40 -60

δ8 − 1.96= −57%

-80

(E) 0.4 A405 of lab reader

1.0

0 -20

0.0 0.2

0.4 0.6 AB of HiSOP

0.8

Linear Correlation for PDV (N=9) y = 1.029x R² = 0.999

0.3 0.2

0

0.2 0.4 0.6 Mean absorbance

0.8

B&A Plot (PDV) δ8 + 2= 6%

10 5

δ8 −2%

0

100% Agree

-5

-10

0.1

δ8 − 2= −10%

-15

0.0 0.0

(F) A405 of lab reader


B&A Plot (CLRV) δ8 + 1.96= 24%

6

Linear Correlation for ApMV (N=112)

0.8

0.0

Assessment of the HiSOP by field immunoassays of plant viruses. To assess the feasibility and the reliability of the HiSOP in field testing, six types of plant viruses were tested by three ELISA methods, including direct, indirect, and DAS ELISA. The absorbance in B channel (AB) by the HiSOP and at 405 nm wavelength (A405) by the lab reader was measured. Linear correlation and difference plots are shown in Figure 7 to evaluate degrees of agreement with both instruments. First, direct ELISA was applied to detect GLRaV3 with 250 samples and CLRV with 115 samples in row (A) and (B) (Figure 7). The linear correlations are achieved to 96.3% and 96.5% respectively. In B&A plots, 96.0% difference data points of GLRaV3 and 98.3% difference data points of CLRV are within limits of agreements. Second, indirect ELISA was utilized to assay BCMC-POTY virus with 535 samples. In row (C) (Figure 7), the linear correlation is 98.7% and 98.5% difference data points are within the limit of agreements. Third, DAS ELISA was tested for ApMV with 112 samples, PDV with 9 samples and PNRSV with 9 samples. In row (D), (E) and (F) (Figure 7), the linear correlations are up to 96.2%, 99.9%, and 98.9%. In B&A plots, 100% difference data points of ApMV, PDV and PNRSV respectively are within the limit of agreements. All individual absorbance values of each samples read by the HiSOP and the lab readers are shown in Figures S1 ~ S6 (Supporting Information). According to ISTA recommends, positive samples were labeled in the yellow

2 3 4 AB of HiSOP

0.2

40

0.1 0.0

B&A Plot (Human IL-6) δ8 + 2= 17%

0.0

y = 1.058x R² = 0.965

0.2

100

δ8 − 1.96= −28%

-40

60

0.3

96.0% Agree

-20

Linear Correlation for CLRV (N=115)

IL-6 Conc. (pg/mL)

Linear Correlation (Human IL-6)

2.0

0.4

δ8 3%

0

-60

0.8

0.0 10

IL-6 Conc. (pg/mL)

0.5

A405 of lab reader

10

0.6

20


1

A=2.622 B=0.068 C=0.442 k=5.797 R 2=0.984

A405 of lab reader

1.4

A450

B

1.2

1.6

0.4 0.6 AB of HiOPS

(B)

A405 of lab reader

1.4

A

1.8

A=2.100 B=0.088 C=0.467 k=7.682 R2=0.991

1.6

0.2

δ8 + 1.96= 35%

40


HiSOP(N=14) (N=14) PAIS

Difference percent(%)

0.6

B&A Plot (GLRaV3)

60 Difference percent (%)

Linear Correlation for GLRaV3 (N =250)

0.8


(A)

0.1

0.2 0.3 0.4 AB of HiSOP

Linear Correlation for PNRSV (N=9)

0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

y = 1.230x R² = 0.989

0.0

0.1

0.2 0.3 0.4 AB of HiSOP

0.5

0.0

0.5

0.6

0.1


0.5

B&A Plot (PNRSV) Difference percent (%)

A and B the maximum and minimum asymptote, respectively. C is the inflection point, and k is the Hill slope to describe the steepness of the fitting curves. The good-of-fitness achieved 99.1% by the HiSOP and 98.4% by the lab reader. The readouts of the both instruments showed strong correlation up to 99.3%. In a B&A plot, all data points (100%) are within the limits of agreement. All analysis results in Figure 6 can be evaluated as a very good agreement in immunoassays. We have validated our HiSOP reader as a reliable instrument compared with a traditional lab reader, showing quantitatively ability and strong agreements in immunoassays.

A450 of lab reader

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A405 of lab reader

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δ8 + 2= 23%

30 10

δ8 −5%

-10 -30

100% Agree

δ8 − 2= −33%

-50 0.0


0.8

Figure 7. Linear correlation and B&A plots for (A) GLRaV3 assayed by direct ELISA with 250 samples, (B) CLRV assayed by direct ELISA with 115 samples, (C) BCMV-POTY virus assayed by indirect ELISA with 535 samples, (D) ApMV assayed by DAS ELISA with 112 samples, and (E) PDV and (F) PNRSV assayed by DAS ELISA with 9 samples, respectively.


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Page 6 of 7

CONCLUSSIONS

Corresponding Author

In this work, we demonstrated a high-throughput smartphone optical sensing platform (HiSOP) to successfully detect high sensitive and reliable immunoassaying. The designed microprism array is utilized to achieve a one-time screening of 64 samples. This platform also has the potential to expand the view to complete 96 wells by using a larger size vacuum forming machine to fabricate a prism array of 96 prisms. Combining 3D printing and vacuum forming to fabricate the PDMS prism array, 3D printing the optical components, and assembling a LED backlight panel, our developed HiSOP can be produced at an affordable cost; estimated total cost for the current device is ~ $50 USD. We performed colorimetric analysis to validate the accuracy of color imaging processing on HiSOP. The linear regressions of the HiSOP results read in green channel (98.2%) and blue channel (99.2%) are comparable to that of the lab reader. For quantifying immunoassays, the sigmoidal dose-response curve is fitted very well by a 4PL equation. The good-of-fitness achieved is 99.1% by our HiSOP which is comparable to the lab reader (98.4%). In a B&A plot, 100 % difference data points of IL-6 are within the limits of agreements. In these tests, the HiSOP has demonstrated accurate quantification ability with strong reliability. Such good performance results from solving the FOV unmatching problem by utilizing the designed microprism array. The ultimate goal for the developing the HiSOP is to meet MPOC needs in situ. Therefore, we field-tested six types of plant viruses using a total of 1,030 plant samples and assayed by three ELISA methods. The linear correlations were 96.2% ~ 99.9% with a high degree of agreement to the lab instrument. These results sufficiently show that the HiSOP is capable as a new alternative to traditional lab readers with high reliability. All immunoassaying results support that the HiSOP has shown potential for other diseases which are tested via ELISA methods, such as human or animal diseases. HiSOP can also be applied for detecting viruses, pathogens, biomarkers, toxin or contaminates tested via immunoassays. The HiSOP is developed for high-throughput screening in less-resourced areas, outside laboratories, and in the field such that laboratory testing could be decentralized without bulky lab instruments. Furthermore, the size and low-cost fabrication of the HiSOP is adequate to meet MPOCT. The design of this HiSOP is appropriate for mass production and the cost can be further reduced by using manufacturing methods such as injection molding. With the HiSOP, clinicians, physicians, professionals, et al. are able to diagnose patients and/or sick animals/plants on-site, or detect environmental pollutions in the field without a long turnaround time. Such on-site diagnosis will speed up the decision-making process with the immediately obtained results. This will advance the routine diagnostic paradigm by decentralizing laboratory testing.

*Author to whom correspondence should be addressed; E-mail: [email protected]; Tel.:+1-509-335-4034.

ASSOCIATED CONTENT Supporting Information Prism apex angle (Table S1) lists the apex angle of each microprism. All absorbance values of individual plant virus field-testing samples read by PAIS and the lab readers are shown in Figure S1 ~S6. The Supporting Information is available free of charge on the ACS Publications website.

Author Contributions The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.

ACKNOWLEDGMENT The authors would like to acknowledge and thank Mr. Craig Owen (Washington State University) for his assistance in reviewing the manuscript and refining the English writing. The authors also like to thank the Frank Innovation Zone (FIZ) at WSU for helping 3D printing components.

REFERENCES

(1) Pai, N. P.; Vadnais, C.; Denkinger, C.; Engel, N.; Pai, M. PLoS Med. 2012, 9, e1001306. (2) Holland, C. A.; Kiechle, F. L. Curr. Opin. Microbiol. 2005, 8, 504-509. ( 3 ) WHO Global Obsercatory for eHealth. New Horizons for Health through Mobile Technologies. Geneva: World Health Organization; 2011:112. (4) Oncescu, V.; O'Dell, D.; Erickson, D. Lab Chip. 2013, 13, 3232-3238. (5) Wei, Q.; Nagi, R.; Sadeghi, K.; Feng, S.; Yan, E.; Ki, S. J.; Caire, R.; Tseng, D.; Ozcan, A. ACS Nano. 2014, 8, 1121-1129.. (6) Wang, L. J.; Chang, Y. C.; Ge, X.; Osmanson, A. T.; Du, D.; Lin, Y.; Li, L. ACS Sensors. 2016, 1, 366-373. (7) Long K. D., Yu H., Cunningham B.T. Biomed Opt Express. 2014, 5, 3792-3806. (8) Preechaburana, P.; Gonzalez, M. C.; Suska, A.; Filippini, D. Angew. Chem. Int. Ed. 2012, 51, 11585-11588. (9) Wojtczak, J.; Bonadonna, P. Am J Emerg Med. 2013, 31, 573577. (10) Lee, S. A.; Yang, C. Lab Chip. 2014, 14, 3056-3063. (11) Berg, B.; Cortazar, B.; Tseng, D.; Ozkan, H.; Feng, S.; Wei, Q.; Chan, R. Y.; Burbano, J.; Farooqui, Q.; Lewinski, M.; Di Carlo, D.; ACS Nano. 2015, 9, 7857-7866. (12) L. Abriola, M. Chin, P. Fuerst, R. Schweitzer and M.A. Sills, J. Biomol. Screen., 1999, 4, 121. (13) P. Durai Arun, K. Sankaran and S. Muttan, Euro. J. Biomed. Int., 2013, 9, 58. (14)Vashist, S. K.; van Oordt, T.; Schneider, E. M.; Zengerle, R.; von, Stetten, F.; Luong, J. H. Biosens. Bioelectron. 2015, 67, 248255. (15) Watson, E. A.; Whitaker III, W. E.; Brewer, C. D.; Harris, S. R. In Aerospace Conference Proceedings, IEEE 2002, 3, 3-1429. (16) Li, L.; Allen, Y. Y. Appl. Opt. 2012, 51, 1843-1852. (17) Li, L.; Allen, Y. Y. Opt. Express. 2010, 18, 18125-18137. (18) Knüpfer, H.; Preiß, R. Breast Cancer Res. Treat. 2007, 102, 129-135. (19) Sullivan, N. J.; Sasser, A. K.; Axel, A.; Vesuna, F.; Raman, V.; Ramirez, N.; Oberyszyn, T. M.; Hall, B. M. Oncogene. 2009, 28, 2940-2947. (20) Michalaki, V.; Syrigos, K.; Charles, P.; Waxman, J. Br. J. Cancer. 2004, 90, 2312-2316. (21) Giri, D.; Ozen, M.; Ittmann, M. Am. J. Pathol. 2001, 159, 2159-2165.

AUTHOR INFORMATION ACS Paragon Plus Environment

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(22) Park, E. J.; Lee, J. H.; Yu, G. Y.; He, G.; Ali, S. R.; Holzer, R. G.; Österreicher, C. H.; Takahashi, H.; Karin, M. Cell. 2010, 140, 197-208. (23) Naugler, W. E.; Sakurai, T.; Kim, S.; Maeda, S.; Kim, K.; Elsharkawy, A. M.; Karin, M. Science. 2007, 317, 121-124. (24) Schafer, Z. T.; Brugge, J. S. J. Clin. Invest. 2007, 117, 36603663. (25) Brichory, F. M.; Misek, D. E.; Yim, A. M.; Krause, M. C.; Giordano, T. J.; Beer, D. G.; Hanash, S. M. PNAS. 2001, 98, 9824-9829. (26) Yanagawa, H.; Sone, S.; Takahashi, Y.; Haku, T.; Yano, S.; Shinohara, T.; Ogura, T. Br. J. Cancer. 1995, 71, 1095. (27) Fang, Y.; Ramasamy, R. P. Biosensors. 2015, 5, 537-561. (28) García-Arenal, F.; Fraile, A.; Malpica, J. M. Annu. Rev. Phytopathol. 2001, 39, 157-186. (29) Lecoq, H.; Moury, B.; Desbiez, C.; Palloix, A.; Pitrat, M. Virus Res. 2004, 100, 31-39. (30) Clark, M. F.; Adams, A. N. J. Gen. Virol. 1977, 34, 475-483. (31) Sutula C. L., Gillett J. M., Morrissey S. M., Ramsdell D. C. Plant Dis. 1986, 70, 722-726. (32) User Guide: Human IL-6 ELISA Kit Manuals & Protocols. https://www.thermofisher.com/order/catalog/product/EH2IL62?I CID=search-product. (33) Maree H. J.; Almeida R. P.; Bester R.; Chooi K. M.; Cohen D.; Dolja V. V.; Fuchs M. F.; Golino D. A.; Jooste A. E.; Martelli G. P.; Naidu R. A. Closteroviridae. 2014, 138. (34) Naidu R. A.; Maree H. J.; Burger J. T. Annu. Rev. Phytopathol. 2015, 53, 613-634. (35) Mink G. I. Annu. Rev. Phytopathol. 1993, 31, 375-402. (36) Cembali T.; Folwell R. J.; Wandschneider P.; Eastwell K. C.; Howell W. E. Crop Prot. 2003, 22,1149-1156. (37) Bandte M.; Büttner C. Pflanzenschutzberichte. 2000, 59, 1-9. (38) Ling K. S.; Zhu H. Y.; Petrovic N.; Gonsalves D. Am. J. Enol. Vitic. 2001, 52,21-27. (39) Massalski T. L.; Cooper J. I.; Hellen C. U.; Edwards M. L. Ann. App. Biol. 1988, 112, 415-26. (40) Forster R. L.; Meyers J. R.; Berger P. H. Bean Common Mosaic Virus. University of Idaho Cooperative Extension Service; 1991. (41) Revers F.; Le Gall O.; Candresse T.; Maule A. J. Mol. Plant Microbe Interact. 1999, 12, 367-376. (42) Sáiz M.; De Blas C.; Carazo G.; Fresno J.; Romero J.; Castro S. Plant Dis. 1995, 79, 79-81. (43) Larsen R. C.; Miklas P. N.; Druffel K. L.; Wyatt S. D. Phytopathology. 2005, 95, 1037-1042. (44) Knapp E.; Hanzer V.; Weiss H. H.; da Câmara Machado A.; Weiss B.; Wang Q., Katinger H.; Laimer da Câmara Machado M. InXVI International Symposium on Fruit Tree Virus diseases 386, 1994, 409-418. (45) Desvignes J. C.; Grasseau N.; Boye R., Cornaggia D.; Aparicio F.; Di Serio F.; Flores R. Plant Dis. 1999, 83, 768-72. (46) Nakamura K.; Yamagishi N.; Isogai M.; Komori S.; Ito T.; Yoshikawa N. J. Gen. Plant Pathol. 2011, 77, 48-53. (47) Moury B.; Cardin L.; Onesto J. P.; Candresse T.; Poupet A. Phytopathology. 2001, 91, 84-91. (48) Gilmer R.; Way R. Phytopathology. 1960, 50, 624-625. (49) Mink G. I.; Aichele M. D. Plant Dis. 1984, 68, 378-381. ( 50 ) Abou-Jawdah Y.; Sobh H.; Cordahi N.; Kawtharani H.; Nemer G.; Maxwell D. P.; Nakhla M. K. J. Virol. Methods. 2004, 121, 31-8. (51) International Rules for Seed Testing. https://www.seedtest.org/en/international-rules-_content---1-1083.html.

(52) Bland, J. M.; Altman, D. G. The Lancet, 1986, 307–310. (53) Giavarina, D. Biochem. Med. 2015, 25, 141-151. ( 54 ) Krippendorff, K. Content analysis: An introduction to its methodology, 3rd Edition. Thousand Oaks, CA: Sage. 2013, 221– 250.


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High-Throughput Optical Sensing Immunoassays on Smartphone

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